Stochastic short-term hydropower planning with inflow scenario trees
作者: Sara SéguinStein-Erik FletenPascal CôtéAlois PichlerCharles Audet
作者单位: 1Department of Mathematics and Industrial Engineering, École Polytechnique de Montréal, C.P. 6079, succ. Centre-ville, Montréal, Québec H3C 3A7, Canada
2GERAD, 3000 ch. de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada
3Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim NO-7491, Norway
4Rio Tinto, 1954 Davis, Saguenay, Québec G7S 4R7, Canada
5Chemnitz University of Technology, Faculty of Mathematics, 09107 Chemnitz, Germany
刊名: European Journal of Operational Research, 2016
来源数据库: Elsevier Journal
DOI: 10.1016/j.ejor.2016.11.028
关键词: Large scale optimizationNonlinear programmingOR in energyScenariosStochastic programming
原始语种摘要: Abstract(#br)This paper presents an optimization approach to solve the short-term hydropower unit commitment and loading problem with uncertain inflows. A scenario tree is built based on a forecasted fan of inflows, which is developed using the weather forecast and the historical weather realizations. The tree-building approach seeks to minimize the nested distance between the stochastic process of historical inflow data and the multistage stochastic process represented in the scenario tree. A two-phase multistage stochastic model is used to solve the problem. The proposed approach is tested on a 31 day rolling-horizon with daily forecasted inflows for three power plants situated in the province of Quebec, Canada, that belong to the company Rio Tinto.
全文获取路径: Elsevier  (合作)
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影响因子:2.038 (2012)

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